Artificial Bee Colony Algorithm with Adaptive Parameter Space Dimension: A Promising Tool for Geophysical Electromagnetic Induction Inversion

Abstract

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method.

The discovery of the church of Rungholt, a landmark for the drowned medieval landscapes of the Wadden Sea World Heritage

“…The cores were analyzed for magnetic susceptibility values using a Bartington MS3 instrument and MS2K sensor. At each coring site, EMI conductivity inversions were performed using the code of 65 , fitting theoretical data based on a 1D conductivity depth model to the three measured apparent conductivity values. The inverted conductivity as well as the measured susceptibilities were used to calculate theoretical IP data values.…”

We Came for the Lake—Late Pleistocene Landscape Reconstruction in Lieth Moor, District Pinneberg, Germany

“…Beyond the mapping of apparent conductivity, we also performed independent 1D conductivity inversions for every 2.5 m by 2.5 m sized bin of the dataset. The minimisation of misfit was implemented by using a 1D stochastic optimisation approach that combines dimension adapting Reversible Jump Markov Chain Monte Carlo with Artificial Bee Colony optimization [85]. Several solution models of simplified model geometry and a variable number of model knots are found by the code for each 1D inversion.…”

Down to the Rivers: A Geophysical Investigation at Étiolles (France) to Reconstruct the Magdalenian Occupation

“…Beyond EMI mapping, we performed 1D conductivity inversions on the whole area for each grid cell of 1 m × 1 m that were subsequently stitched together. Inversion was carried out using a 1D stochastic optimization approach that combines dimension-adapting Reversible Jump Markov Chain Monte Carlo (MCMC) with Artificial Bee Colony (ABC) optimization [37]. Several solution models of simplified model geometry and a variable number of model knots were found by the code for each 1D inversion.…”